Traditional scatter plots have been widely used to display correlation or association between two variables (or attributes). A scatter plot is a chart that uses Cartesian coordinates (e.g., x-axis or y-axis coordinates) to display values for the two variables. The data displayed in the scatter plot is a collection of points, each having one coordinate on the horizontal axis and one on the vertical axis. An example of a traditional scatter plot is depicted in FIG. 1, where the horizontal axis variable represented in the example of FIG. 1 is a first temperature (T1) from a first temperature sensor (e.g., for monitoring temperature in a first portion of a system), and the vertical axis corresponds to a second temperature (T2) from a second temperature sensor (e.g., for monitoring temperature in a second portion of the system). In the example of FIG. 1, each point in the scatter plot represents a data record that contains a particular pair of T1 and T2 values (which represent coordinates in the scatter plot).
Various points correlating the T1 and T2 values are plotted in the scatter plot of FIG. 1. In FIG. 1, most of the points are clumped together in a relatively dense region 100. The points that are clumped together in the dense region 100 which have the same coordinate values for T1 and T2 lay on top of each other, which results in occlusion of such points. Occlusion prevents a user from seeing the true number of points in dense regions of the scatter plot. Effectively, a traditional scatter plot can show just a relatively small number of distinct data points, even though there may be a much larger number of data points that the viewer cannot see as a result of occlusion (due to overlay of data points). Such occlusion of data points can hide the true extent of the relationship between different variables in a traditional scatter plot.